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Reference: Acharya V. and Nagarajaram H.A. Hansa: An automated method for discriminating disease and neutral human nsSNPs. Human Mutation (2012) 2:332-337.
Hosted: Developed and maintained by the Laboratory of Computational Biology, Centre for DNA fingerprinting and diagnostics, Nampally, Hyderabad. (http://hansa.cdfd.org.in:8080/)

Hansa uses a support vector machine (SVM) trained on a combination of position-specific, structural and amino acid features.

Hansa is trained on the HumVar mutation data also implemented in the PhD-SNP and Parepro algorithms. This dataset comprises 13032 disease-related substitutions from 1111 genes and 8946 neutral substitutions from 3484 genes.
Hansa combines 10 different properties of these substitutions to partition disease and neutral mutations.
• 6 features related to the specific position of the mutation and probabilities of the amino acids.
• 2 features of protein structural environment.
• 2 features based on likelihood of the amino acid substitutions.

Alignments for the query protein are generated using PSI-BLAST.

The user can provide a database ID, GenBank, RefSeq, SWISSPROT or PDB identifier for the query protein. Alternatively the protein sequence can be pasted or uploaded. The mutation(s) can then be provided.

Mutations are predicted as ‘disease’ or ‘neutral’ and a breakdown of each of the parameter scores are provided.